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import warnings
from py4j.protocol import Py4JJavaError
from pyspark.rdd import RDD
from pyspark.storagelevel import StorageLevel
from pyspark.serializers import AutoBatchedSerializer, PickleSerializer, PairDeserializer, \
NoOpSerializer
from pyspark.streaming import DStream
from pyspark.streaming.dstream import TransformedDStream
from pyspark.streaming.util import TransformFunction
__all__ = ['Broker', 'KafkaMessageAndMetadata', 'KafkaUtils', 'OffsetRange',
'TopicAndPartition', 'utf8_decoder']
[docs]def utf8_decoder(s):
""" Decode the unicode as UTF-8 """
if s is None:
return None
return s.decode('utf-8')
[docs]class KafkaUtils(object):
[docs] @staticmethod
def createStream(ssc, zkQuorum, groupId, topics, kafkaParams=None,
storageLevel=StorageLevel.MEMORY_AND_DISK_2,
keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
"""
Create an input stream that pulls messages from a Kafka Broker.
:param ssc: StreamingContext object
:param zkQuorum: Zookeeper quorum (hostname:port,hostname:port,..).
:param groupId: The group id for this consumer.
:param topics: Dict of (topic_name -> numPartitions) to consume.
Each partition is consumed in its own thread.
:param kafkaParams: Additional params for Kafka
:param storageLevel: RDD storage level.
:param keyDecoder: A function used to decode key (default is utf8_decoder)
:param valueDecoder: A function used to decode value (default is utf8_decoder)
:return: A DStream object
.. note:: Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0.
See SPARK-21893.
"""
warnings.warn(
"Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0. "
"See SPARK-21893.",
DeprecationWarning)
if kafkaParams is None:
kafkaParams = dict()
kafkaParams.update({
"zookeeper.connect": zkQuorum,
"group.id": groupId,
"zookeeper.connection.timeout.ms": "10000",
})
if not isinstance(topics, dict):
raise TypeError("topics should be dict")
jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
helper = KafkaUtils._get_helper(ssc._sc)
jstream = helper.createStream(ssc._jssc, kafkaParams, topics, jlevel)
ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
stream = DStream(jstream, ssc, ser)
return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
[docs] @staticmethod
def createDirectStream(ssc, topics, kafkaParams, fromOffsets=None,
keyDecoder=utf8_decoder, valueDecoder=utf8_decoder,
messageHandler=None):
"""
Create an input stream that directly pulls messages from a Kafka Broker and specific offset.
This is not a receiver based Kafka input stream, it directly pulls the message from Kafka
in each batch duration and processed without storing.
This does not use Zookeeper to store offsets. The consumed offsets are tracked
by the stream itself. For interoperability with Kafka monitoring tools that depend on
Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
You can access the offsets used in each batch from the generated RDDs (see
To recover from driver failures, you have to enable checkpointing in the StreamingContext.
The information on consumed offset can be recovered from the checkpoint.
See the programming guide for details (constraints, etc.).
:param ssc: StreamingContext object.
:param topics: list of topic_name to consume.
:param kafkaParams: Additional params for Kafka.
:param fromOffsets: Per-topic/partition Kafka offsets defining the (inclusive) starting
point of the stream.
:param keyDecoder: A function used to decode key (default is utf8_decoder).
:param valueDecoder: A function used to decode value (default is utf8_decoder).
:param messageHandler: A function used to convert KafkaMessageAndMetadata. You can assess
meta using messageHandler (default is None).
:return: A DStream object
.. note:: Experimental
.. note:: Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0.
See SPARK-21893.
"""
warnings.warn(
"Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0. "
"See SPARK-21893.",
DeprecationWarning)
if fromOffsets is None:
fromOffsets = dict()
if not isinstance(topics, list):
raise TypeError("topics should be list")
if not isinstance(kafkaParams, dict):
raise TypeError("kafkaParams should be dict")
def funcWithoutMessageHandler(k_v):
return (keyDecoder(k_v[0]), valueDecoder(k_v[1]))
def funcWithMessageHandler(m):
m._set_key_decoder(keyDecoder)
m._set_value_decoder(valueDecoder)
return messageHandler(m)
helper = KafkaUtils._get_helper(ssc._sc)
jfromOffsets = dict([(k._jTopicAndPartition(helper),
v) for (k, v) in fromOffsets.items()])
if messageHandler is None:
ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
func = funcWithoutMessageHandler
jstream = helper.createDirectStreamWithoutMessageHandler(
ssc._jssc, kafkaParams, set(topics), jfromOffsets)
else:
ser = AutoBatchedSerializer(PickleSerializer())
func = funcWithMessageHandler
jstream = helper.createDirectStreamWithMessageHandler(
ssc._jssc, kafkaParams, set(topics), jfromOffsets)
stream = DStream(jstream, ssc, ser).map(func)
return KafkaDStream(stream._jdstream, ssc, stream._jrdd_deserializer)
[docs] @staticmethod
def createRDD(sc, kafkaParams, offsetRanges, leaders=None,
keyDecoder=utf8_decoder, valueDecoder=utf8_decoder,
messageHandler=None):
"""
Create an RDD from Kafka using offset ranges for each topic and partition.
:param sc: SparkContext object
:param kafkaParams: Additional params for Kafka
:param offsetRanges: list of offsetRange to specify topic:partition:[start, end) to consume
:param leaders: Kafka brokers for each TopicAndPartition in offsetRanges. May be an empty
map, in which case leaders will be looked up on the driver.
:param keyDecoder: A function used to decode key (default is utf8_decoder)
:param valueDecoder: A function used to decode value (default is utf8_decoder)
:param messageHandler: A function used to convert KafkaMessageAndMetadata. You can assess
meta using messageHandler (default is None).
:return: An RDD object
.. note:: Experimental
.. note:: Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0.
See SPARK-21893.
"""
warnings.warn(
"Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0. "
"See SPARK-21893.",
DeprecationWarning)
if leaders is None:
leaders = dict()
if not isinstance(kafkaParams, dict):
raise TypeError("kafkaParams should be dict")
if not isinstance(offsetRanges, list):
raise TypeError("offsetRanges should be list")
def funcWithoutMessageHandler(k_v):
return (keyDecoder(k_v[0]), valueDecoder(k_v[1]))
def funcWithMessageHandler(m):
m._set_key_decoder(keyDecoder)
m._set_value_decoder(valueDecoder)
return messageHandler(m)
helper = KafkaUtils._get_helper(sc)
joffsetRanges = [o._jOffsetRange(helper) for o in offsetRanges]
jleaders = dict([(k._jTopicAndPartition(helper),
v._jBroker(helper)) for (k, v) in leaders.items()])
if messageHandler is None:
jrdd = helper.createRDDWithoutMessageHandler(
sc._jsc, kafkaParams, joffsetRanges, jleaders)
ser = PairDeserializer(NoOpSerializer(), NoOpSerializer())
rdd = RDD(jrdd, sc, ser).map(funcWithoutMessageHandler)
else:
jrdd = helper.createRDDWithMessageHandler(
sc._jsc, kafkaParams, joffsetRanges, jleaders)
rdd = RDD(jrdd, sc).map(funcWithMessageHandler)
return KafkaRDD(rdd._jrdd, sc, rdd._jrdd_deserializer)
@staticmethod
def _get_helper(sc):
try:
return sc._jvm.org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper()
except TypeError as e:
if str(e) == "'JavaPackage' object is not callable":
KafkaUtils._printErrorMsg(sc)
raise
@staticmethod
def _printErrorMsg(sc):
print("""
________________________________________________________________________________________________
Spark Streaming's Kafka libraries not found in class path. Try one of the following.
1. Include the Kafka library and its dependencies with in the
spark-submit command as
$ bin/spark-submit --packages org.apache.spark:spark-streaming-kafka-0-8:%s ...
2. Download the JAR of the artifact from Maven Central http://search.maven.org/,
Group Id = org.apache.spark, Artifact Id = spark-streaming-kafka-0-8-assembly, Version = %s.
Then, include the jar in the spark-submit command as
$ bin/spark-submit --jars <spark-streaming-kafka-0-8-assembly.jar> ...
________________________________________________________________________________________________
""" % (sc.version, sc.version))
[docs]class OffsetRange(object):
"""
Represents a range of offsets from a single Kafka TopicAndPartition.
.. note:: Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0.
See SPARK-21893.
"""
def __init__(self, topic, partition, fromOffset, untilOffset):
"""
Create an OffsetRange to represent range of offsets
:param topic: Kafka topic name.
:param partition: Kafka partition id.
:param fromOffset: Inclusive starting offset.
:param untilOffset: Exclusive ending offset.
"""
warnings.warn(
"Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0. "
"See SPARK-21893.",
DeprecationWarning)
self.topic = topic
self.partition = partition
self.fromOffset = fromOffset
self.untilOffset = untilOffset
def __eq__(self, other):
if isinstance(other, self.__class__):
return (self.topic == other.topic
and self.partition == other.partition
and self.fromOffset == other.fromOffset
and self.untilOffset == other.untilOffset)
else:
return False
def __ne__(self, other):
return not self.__eq__(other)
def __str__(self):
return "OffsetRange(topic: %s, partition: %d, range: [%d -> %d]" \
% (self.topic, self.partition, self.fromOffset, self.untilOffset)
def _jOffsetRange(self, helper):
return helper.createOffsetRange(self.topic, self.partition, self.fromOffset,
self.untilOffset)
[docs]class TopicAndPartition(object):
"""
Represents a specific topic and partition for Kafka.
.. note:: Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0.
See SPARK-21893.
"""
def __init__(self, topic, partition):
"""
Create a Python TopicAndPartition to map to the Java related object
:param topic: Kafka topic name.
:param partition: Kafka partition id.
"""
warnings.warn(
"Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0. "
"See SPARK-21893.",
DeprecationWarning)
self._topic = topic
self._partition = partition
def _jTopicAndPartition(self, helper):
return helper.createTopicAndPartition(self._topic, self._partition)
def __eq__(self, other):
if isinstance(other, self.__class__):
return (self._topic == other._topic
and self._partition == other._partition)
else:
return False
def __ne__(self, other):
return not self.__eq__(other)
def __hash__(self):
return (self._topic, self._partition).__hash__()
[docs]class Broker(object):
"""
Represent the host and port info for a Kafka broker.
.. note:: Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0.
See SPARK-21893.
"""
def __init__(self, host, port):
"""
Create a Python Broker to map to the Java related object.
:param host: Broker's hostname.
:param port: Broker's port.
"""
warnings.warn(
"Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0. "
"See SPARK-21893.",
DeprecationWarning)
self._host = host
self._port = port
def _jBroker(self, helper):
return helper.createBroker(self._host, self._port)
class KafkaRDD(RDD):
"""
A Python wrapper of KafkaRDD, to provide additional information on normal RDD.
.. note:: Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0.
See SPARK-21893.
"""
def __init__(self, jrdd, ctx, jrdd_deserializer):
warnings.warn(
"Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0. "
"See SPARK-21893.",
DeprecationWarning)
RDD.__init__(self, jrdd, ctx, jrdd_deserializer)
def offsetRanges(self):
"""
Get the OffsetRange of specific KafkaRDD.
:return: A list of OffsetRange
"""
helper = KafkaUtils._get_helper(self.ctx)
joffsetRanges = helper.offsetRangesOfKafkaRDD(self._jrdd.rdd())
ranges = [OffsetRange(o.topic(), o.partition(), o.fromOffset(), o.untilOffset())
for o in joffsetRanges]
return ranges
class KafkaDStream(DStream):
"""
A Python wrapper of KafkaDStream
.. note:: Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0.
See SPARK-21893.
"""
def __init__(self, jdstream, ssc, jrdd_deserializer):
warnings.warn(
"Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0. "
"See SPARK-21893.",
DeprecationWarning)
DStream.__init__(self, jdstream, ssc, jrdd_deserializer)
def foreachRDD(self, func):
"""
Apply a function to each RDD in this DStream.
"""
if func.__code__.co_argcount == 1:
old_func = func
func = lambda r, rdd: old_func(rdd)
jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer) \
.rdd_wrapper(lambda jrdd, ctx, ser: KafkaRDD(jrdd, ctx, ser))
api = self._ssc._jvm.PythonDStream
api.callForeachRDD(self._jdstream, jfunc)
def transform(self, func):
"""
Return a new DStream in which each RDD is generated by applying a function
on each RDD of this DStream.
`func` can have one argument of `rdd`, or have two arguments of
(`time`, `rdd`)
"""
if func.__code__.co_argcount == 1:
oldfunc = func
func = lambda t, rdd: oldfunc(rdd)
assert func.__code__.co_argcount == 2, "func should take one or two arguments"
return KafkaTransformedDStream(self, func)
class KafkaTransformedDStream(TransformedDStream):
"""
Kafka specific wrapper of TransformedDStream to transform on Kafka RDD.
.. note:: Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0.
See SPARK-21893.
"""
def __init__(self, prev, func):
warnings.warn(
"Deprecated in 2.3.0. Kafka 0.8 support is deprecated as of Spark 2.3.0. "
"See SPARK-21893.",
DeprecationWarning)
TransformedDStream.__init__(self, prev, func)
@property
def _jdstream(self):
if self._jdstream_val is not None:
return self._jdstream_val
jfunc = TransformFunction(self._sc, self.func, self.prev._jrdd_deserializer) \
.rdd_wrapper(lambda jrdd, ctx, ser: KafkaRDD(jrdd, ctx, ser))
dstream = self._sc._jvm.PythonTransformedDStream(self.prev._jdstream.dstream(), jfunc)
self._jdstream_val = dstream.asJavaDStream()
return self._jdstream_val
